Analysis of GLDS-38 from NASA GeneLab
This R markdown file was auto-generated by the iDEP website Using iDEP 0.91, originally by Steven Xijin.Ge@sdstate.edu
Ge SX, Son EW, Yao R: iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics 2018, 19(1):534. PMID:30567491
First we set up the working directory to where the files are saved.
input_biclustMethod_ <- "BCCC()"
R packages used
library(RSQLite, verbose = FALSE) # for database connection
library(gplots, verbose = FALSE) # for hierarchical clustering
library(ggplot2, verbose = FALSE) # graphics
library(e1071, verbose = FALSE) # computing kurtosis
#library(DT, verbose = FALSE) # for renderDataTable
library(plotly, verbose = FALSE) # for interactive heatmap
library(reshape2, verbose = FALSE) # for melt correlation matrix in heatmap
# From Data Read Function
library(edgeR, verbose = FALSE) # count data D.E.
library(DESeq2, verbose = FALSE) # count data analysis, DEG.DESeq2
# TSNE Plot, tSNEgenePlot
library(Rtsne, verbose = FALSE)
# PGSA Pathway PGSEA Pathway, PGSEAplot
library(PGSEA, verbose = FALSE)
## Warning: Package 'KEGG.db' is deprecated and will be removed from Bioconductor
## version 3.12
# DEG.limma
library(limma, verbose = FALSE) # Differential expression
library(statmod, verbose = FALSE)
# enrichment plot
library(dendextend) # customizing tree
# enrich.net2, moduleNetwork
library(igraph)
# Stringdb_geneList, StringDB_GO_enrichmentData, stringDB_network1
# StringDB_network_link
library(STRINGdb, verbose = FALSE)
# gagePathwayData
library(gage, verbose = FALSE) # pathway analysis
# fgseaPathwayData
library(fgsea, verbose = FALSE) # fast GSEA
# ReactomePAPathwayData
library(ReactomePA, verbose = FALSE) # pathway analysis
# KeggImage
library(pathview)
# genomePlot, genomePlotDataPre
library(PREDA, verbose = FALSE) # showing expression on genome
library(PREDAsampledata, verbose = FALSE)
library(hgu133plus2.db, verbose = FALSE)
# biclustering
library(biclust, verbose = FALSE)
library(knitr) # install if needed. for showing tables with kable
library(kableExtra)
if (input_biclustMethod_ == "BCQU()") {
library(QUBIC, verbose = FALSE)
} # have trouble installing on Linux
if (input_biclustMethod_ == "BCUnibic()") {
library(runibic, verbose = FALSE)
} # Test biclustMethod dependant qubic runibic
# wgcna
library(WGCNA)
library(flashClust, verbose = FALSE)
source("iDEP_core_functions_only.R")
# Each row of this matrix represents a color scheme;
mycolors_ <- sort(rainbow(20))[c(1, 20, 10, 11, 2, 19, 3, 12, 4, 13, 5, 14, 6, 15, 7, 16, 8, 17, 9, 18)]
hmcols_ <- colorRampPalette(colors = c('#4575B4', '#91BFDB', '#E0F3F8', '#FFFFBF', '#FEE090', '#FC8D59', '#D73027'))(75)
heatColors_ <- rbind(
greenred(75),
bluered(75),
colorpanel(75, "green", "black", "magenta"),
colorpanel(75, "blue", "yellow", "red"),
hmcols_
)
rownames(heatColors_) <- c("Green-Black-Red", "Blue-White-Red", "Green-Black-Magenta", "Blue-Yellow-Red", "Blue-white-brown")
We are using the downloaded gene expression file where gene IDs has been converted to Ensembl gene IDs. This is because the ID conversion database is too large to download. You can use your original file if your file uses Ensembl ID, or you do not want to use the pathway files available in iDEP (or it is not available).
inputFolderFiles <- list.files(params$input_folder, full.names = TRUE)
inputFile_ <- inputFolderFiles[stringr::str_detect(tolower(inputFolderFiles), "expression.csv$")]
sampleInfoFile_ <- inputFolderFiles[stringr::str_detect(tolower(inputFolderFiles), "sampleinfo.csv$")]
gldsMetadataFile_ <- inputFolderFiles[stringr::str_detect(tolower(inputFolderFiles), "metadata.csv$")]
geneInfoFile_ <- params$geneInfoFile
geneSetFile_ <- params$geneSetFile # pathway database in SQL; can be GMT format
STRING10_speciesFile_ <- "https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/STRING10_species.csv"
readMetadata.out_ <- readMetadata(inFile = gldsMetadataFile_) #gldsMetadataFile_)
kable(readMetadata.out_) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Col0_FLT_Rep1 | Col0_FLT_Rep2 | Col0_FLT_Rep3 | Col0_FLT_Rep4 | Col0_FLT_Rep5 | Col0_FLT_Rep6 | Col0_FLT_Rep7 | Col0_FLT_Rep8 | Col0_GC_Rep1 | Col0_GC_Rep2 | Col0_GC_Rep3 | Col0_GC_Rep4 | Col0_GC_Rep5 | Col0_GC_Rep6 | Col0_GC_Rep7 | Col0_GC_Rep8 | Cvi0_FLT_Rep1 | Cvi0_FLT_Rep2 | Cvi0_FLT_Rep3 | Cvi0_FLT_Rep4 | Cvi0_FLT_Rep5 | Cvi0_FLT_Rep6 | Cvi0_GC_Rep1 | Cvi0_GC_Rep2 | Cvi0_GC_Rep3 | Cvi0_GC_Rep4 | Cvi0_GC_Rep5 | Cvi0_GC_Rep6 | Ler0_FLT_Rep1 | Ler0_FLT_Rep2 | Ler0_FLT_Rep3 | Ler0_FLT_Rep4 | Ler0_FLT_Rep5 | Ler0_FLT_Rep6 | Ler0_GC_Rep1 | Ler0_GC_Rep2 | Ler0_GC_Rep3 | Ler0_GC_Rep4 | Ler0_GC_Rep5 | Ler0_GC_Rep6 | Ws2_FLT_Rep1 | Ws2_FLT_Rep2 | Ws2_FLT_Rep3 | Ws2_FLT_Rep4 | Ws2_FLT_Rep5 | Ws2_FLT_Rep6 | Ws2_FLT_Rep7 | Ws2_FLT_Rep8 | Ws2_GC_Rep1 | Ws2_GC_Rep2 | Ws2_GC_Rep3 | Ws2_GC_Rep4 | Ws2_GC_Rep5 | Ws2_GC_Rep6 | Ws2_GC_Rep7 | Ws2_GC_Rep8 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sample.LongId | Atha.Col.0.sl.pool.FLT.Rep1.R1.FL.A1.RNAseq.RNAseq | Atha.Col.0.sl.pool.FLT.Rep2.R1.FL.A4.RNAseq.RNAseq | Atha.Col.0.sl.pool.FLT.Rep3.R1.FL.B1.RNAseq.RNAseq | Atha.Col.0.sl.pool.FLT.Rep4.R1.FL.B4.RNAseq.RNAseq | Atha.Col.0.sl.pool.FLT.Rep5.R2.FL.A1.RNAseq.RNAseq | Atha.Col.0.sl.pool.FLT.Rep6.R2.FL.A4.RNAseq.RNAseq | Atha.Col.0.sl.pool.FLT.Rep7.R2.FL.B1.RNAseq.RNAseq | Atha.Col.0.sl.pool.FLT.Rep8.R2.FL.B4.RNAseq.RNAseq | Atha.Col.0.sl.pool.GC.Rep1.R1.GC.A1.RNAseq.RNAseq | Atha.Col.0.sl.pool.GC.Rep2.R1.GC.A4.RNAseq.RNAseq | Atha.Col.0.sl.pool.GC.Rep3.R1.GC.B1.RNAseq.RNAseq | Atha.Col.0.sl.pool.GC.Rep4.R1.GC.B4.RNAseq.RNAseq | Atha.Col.0.sl.pool.GC.Rep5.R2.GC.A1.RNAseq.RNAseq | Atha.Col.0.sl.pool.GC.Rep6.R2.GC.A4.RNAseq.RNAseq | Atha.Col.0.sl.pool.GC.Rep7.R2.GC.B1.RNAseq.RNAseq | Atha.Col.0.sl.pool.GC.Rep8.R2.GC.B4.RNAseq.RNAseq | Atha.Cvi.0.sl.pool.FLT.Rep1.R1.FL.C3.RNAseq.RNAseq | Atha.Cvi.0.sl.pool.FLT.Rep2.R1.FL.D2.RNAseq.RNAseq | Atha.Cvi.0.sl.pool.FLT.Rep3.R1.FL.D5.RNAseq.RNAseq | Atha.Cvi.0.sl.pool.FLT.Rep4.R2.FL.C3.RNAseq.RNAseq | Atha.Cvi.0.sl.pool.FLT.Rep5.R2.FL.D2.RNAseq.RNAseq | Atha.Cvi.0.sl.pool.FLT.Rep6.R2.FL.D5.RNAseq.RNAseq | Atha.Cvi.0.sl.pool.GC.Rep1.R1.GC.C3.RNAseq.RNAseq | Atha.Cvi.0.sl.pool.GC.Rep2.R1.GC.D2.RNAseq.RNAseq | Atha.Cvi.0.sl.pool.GC.Rep3.R1.GC.D5.RNAseq.RNAseq | Atha.Cvi.0.sl.pool.GC.Rep4.R2.GC.C3.RNAseq.RNAseq | Atha.Cvi.0.sl.pool.GC.Rep5.R2.GC.D2.RNAseq.RNAseq | Atha.Cvi.0.sl.pool.GC.Rep6.R2.GC.D5.RNAseq.RNAseq | Atha.Ler.0.sl.pool.FLT.Rep1.R1.FL.C2.RNAseq.RNAseq | Atha.Ler.0.sl.pool.FLT.Rep2.R1.FL.C5.RNAseq.RNAseq | Atha.Ler.0.sl.pool.FLT.Rep3.R1.FL.D3.RNAseq.RNAseq | Atha.Ler.0.sl.pool.FLT.Rep4.R2.FL.C2.RNAseq.RNAseq | Atha.Ler.0.sl.pool.FLT.Rep5.R2.FL.C5.RNAseq.RNAseq | Atha.Ler.0.sl.pool.FLT.Rep6.R2.FL.D3.RNAseq.RNAseq | Atha.Ler.0.sl.pool.GC.Rep1.R1.GC.C2.RNAseq.RNAseq | Atha.Ler.0.sl.pool.GC.Rep2.R1.GC.C5.RNAseq.RNAseq | Atha.Ler.0.sl.pool.GC.Rep3.R1.GC.D3.RNAseq.RNAseq | Atha.Ler.0.sl.pool.GC.Rep4.R2.GC.C2.RNAseq.RNAseq | Atha.Ler.0.sl.pool.GC.Rep5.R2.GC.C5.RNAseq.RNAseq | Atha.Ler.0.sl.pool.GC.Rep6.R2.GC.D3.RNAseq.RNAseq | Atha.Ws.2.sl.pool.FLT.Rep1.R1.FL.C1.RNAseq.RNAseq | Atha.Ws.2.sl.pool.FLT.Rep2.R1.FL.C4.RNAseq.RNAseq | Atha.Ws.2.sl.pool.FLT.Rep3.R1.FL.D1.RNAseq.RNAseq | Atha.Ws.2.sl.pool.FLT.Rep4.R1.FL.D4.RNAseq.RNAseq | Atha.Ws.2.sl.pool.FLT.Rep5.R2.FL.C1.RNAseq.RNAseq | Atha.Ws.2.sl.pool.FLT.Rep6.R2.FL.C4.RNAseq.RNAseq | Atha.Ws.2.sl.pool.FLT.Rep7.R2.FL.D1.RNAseq.RNAseq | Atha.Ws.2.sl.pool.FLT.Rep8.R2.FL.D4.RNAseq.RNAseq | Atha.Ws.2.sl.pool.GC.Rep1.R1.GC.C1.RNAseq.RNAseq | Atha.Ws.2.sl.pool.GC.Rep2.R1.GC.C4.RNAseq.RNAseq | Atha.Ws.2.sl.pool.GC.Rep3.R1.GC.D1.RNAseq.RNAseq | Atha.Ws.2.sl.pool.GC.Rep4.R1.GC.D4.RNAseq.RNAseq | Atha.Ws.2.sl.pool.GC.Rep5.R2.GC.C1.RNAseq.RNAseq | Atha.Ws.2.sl.pool.GC.Rep6.R2.GC.C4.RNAseq.RNAseq | Atha.Ws.2.sl.pool.GC.Rep7.R2.GC.D1.RNAseq.RNAseq | Atha.Ws.2.sl.pool.GC.Rep8.R2.GC.D4.RNAseq.RNAseq |
| Sample.Id | Atha.Col.0.sl.pool.FLT.Rep1.R1.FL.A1 | Atha.Col.0.sl.pool.FLT.Rep2.R1.FL.A4 | Atha.Col.0.sl.pool.FLT.Rep3.R1.FL.B1 | Atha.Col.0.sl.pool.FLT.Rep4.R1.FL.B4 | Atha.Col.0.sl.pool.FLT.Rep5.R2.FL.A1 | Atha.Col.0.sl.pool.FLT.Rep6.R2.FL.A4 | Atha.Col.0.sl.pool.FLT.Rep7.R2.FL.B1 | Atha.Col.0.sl.pool.FLT.Rep8.R2.FL.B4 | Atha.Col.0.sl.pool.GC.Rep1.R1.GC.A1 | Atha.Col.0.sl.pool.GC.Rep2.R1.GC.A4 | Atha.Col.0.sl.pool.GC.Rep3.R1.GC.B1 | Atha.Col.0.sl.pool.GC.Rep4.R1.GC.B4 | Atha.Col.0.sl.pool.GC.Rep5.R2.GC.A1 | Atha.Col.0.sl.pool.GC.Rep6.R2.GC.A4 | Atha.Col.0.sl.pool.GC.Rep7.R2.GC.B1 | Atha.Col.0.sl.pool.GC.Rep8.R2.GC.B4 | Atha.Cvi.0.sl.pool.FLT.Rep1.R1.FL.C3 | Atha.Cvi.0.sl.pool.FLT.Rep2.R1.FL.D2 | Atha.Cvi.0.sl.pool.FLT.Rep3.R1.FL.D5 | Atha.Cvi.0.sl.pool.FLT.Rep4.R2.FL.C3 | Atha.Cvi.0.sl.pool.FLT.Rep5.R2.FL.D2 | Atha.Cvi.0.sl.pool.FLT.Rep6.R2.FL.D5 | Atha.Cvi.0.sl.pool.GC.Rep1.R1.GC.C3 | Atha.Cvi.0.sl.pool.GC.Rep2.R1.GC.D2 | Atha.Cvi.0.sl.pool.GC.Rep3.R1.GC.D5 | Atha.Cvi.0.sl.pool.GC.Rep4.R2.GC.C3 | Atha.Cvi.0.sl.pool.GC.Rep5.R2.GC.D2 | Atha.Cvi.0.sl.pool.GC.Rep6.R2.GC.D5 | Atha.Ler.0.sl.pool.FLT.Rep1.R1.FL.C2 | Atha.Ler.0.sl.pool.FLT.Rep2.R1.FL.C5 | Atha.Ler.0.sl.pool.FLT.Rep3.R1.FL.D3 | Atha.Ler.0.sl.pool.FLT.Rep4.R2.FL.C2 | Atha.Ler.0.sl.pool.FLT.Rep5.R2.FL.C5 | Atha.Ler.0.sl.pool.FLT.Rep6.R2.FL.D3 | Atha.Ler.0.sl.pool.GC.Rep1.R1.GC.C2 | Atha.Ler.0.sl.pool.GC.Rep2.R1.GC.C5 | Atha.Ler.0.sl.pool.GC.Rep3.R1.GC.D3 | Atha.Ler.0.sl.pool.GC.Rep4.R2.GC.C2 | Atha.Ler.0.sl.pool.GC.Rep5.R2.GC.C5 | Atha.Ler.0.sl.pool.GC.Rep6.R2.GC.D3 | Atha.Ws.2.sl.pool.FLT.Rep1.R1.FL.C1 | Atha.Ws.2.sl.pool.FLT.Rep2.R1.FL.C4 | Atha.Ws.2.sl.pool.FLT.Rep3.R1.FL.D1 | Atha.Ws.2.sl.pool.FLT.Rep4.R1.FL.D4 | Atha.Ws.2.sl.pool.FLT.Rep5.R2.FL.C1 | Atha.Ws.2.sl.pool.FLT.Rep6.R2.FL.C4 | Atha.Ws.2.sl.pool.FLT.Rep7.R2.FL.D1 | Atha.Ws.2.sl.pool.FLT.Rep8.R2.FL.D4 | Atha.Ws.2.sl.pool.GC.Rep1.R1.GC.C1 | Atha.Ws.2.sl.pool.GC.Rep2.R1.GC.C4 | Atha.Ws.2.sl.pool.GC.Rep3.R1.GC.D1 | Atha.Ws.2.sl.pool.GC.Rep4.R1.GC.D4 | Atha.Ws.2.sl.pool.GC.Rep5.R2.GC.C1 | Atha.Ws.2.sl.pool.GC.Rep6.R2.GC.C4 | Atha.Ws.2.sl.pool.GC.Rep7.R2.GC.D1 | Atha.Ws.2.sl.pool.GC.Rep8.R2.GC.D4 |
| Sample.Name | Atha_Col-0_sl-pool_FLT_Rep1_R1-FL-A1 | Atha_Col-0_sl-pool_FLT_Rep2_R1-FL-A4 | Atha_Col-0_sl-pool_FLT_Rep3_R1-FL-B1 | Atha_Col-0_sl-pool_FLT_Rep4_R1-FL-B4 | Atha_Col-0_sl-pool_FLT_Rep5_R2-FL-A1 | Atha_Col-0_sl-pool_FLT_Rep6_R2-FL-A4 | Atha_Col-0_sl-pool_FLT_Rep7_R2-FL-B1 | Atha_Col-0_sl-pool_FLT_Rep8_R2-FL-B4 | Atha_Col-0_sl-pool_GC_Rep1_R1-GC-A1 | Atha_Col-0_sl-pool_GC_Rep2_R1-GC-A4 | Atha_Col-0_sl-pool_GC_Rep3_R1-GC-B1 | Atha_Col-0_sl-pool_GC_Rep4_R1-GC-B4 | Atha_Col-0_sl-pool_GC_Rep5_R2-GC-A1 | Atha_Col-0_sl-pool_GC_Rep6_R2-GC-A4 | Atha_Col-0_sl-pool_GC_Rep7_R2-GC-B1 | Atha_Col-0_sl-pool_GC_Rep8_R2-GC-B4 | Atha_Cvi-0_sl-pool_FLT_Rep1_R1-FL-C3 | Atha_Cvi-0_sl-pool_FLT_Rep2_R1-FL-D2 | Atha_Cvi-0_sl-pool_FLT_Rep3_R1-FL-D5 | Atha_Cvi-0_sl-pool_FLT_Rep4_R2-FL-C3 | Atha_Cvi-0_sl-pool_FLT_Rep5_R2-FL-D2 | Atha_Cvi-0_sl-pool_FLT_Rep6_R2-FL-D5 | Atha_Cvi-0_sl-pool_GC_Rep1_R1-GC-C3 | Atha_Cvi-0_sl-pool_GC_Rep2_R1-GC-D2 | Atha_Cvi-0_sl-pool_GC_Rep3_R1-GC-D5 | Atha_Cvi-0_sl-pool_GC_Rep4_R2-GC-C3 | Atha_Cvi-0_sl-pool_GC_Rep5_R2-GC-D2 | Atha_Cvi-0_sl-pool_GC_Rep6_R2-GC-D5 | Atha_Ler-0_sl-pool_FLT_Rep1_R1-FL-C2 | Atha_Ler-0_sl-pool_FLT_Rep2_R1-FL-C5 | Atha_Ler-0_sl-pool_FLT_Rep3_R1-FL-D3 | Atha_Ler-0_sl-pool_FLT_Rep4_R2-FL-C2 | Atha_Ler-0_sl-pool_FLT_Rep5_R2-FL-C5 | Atha_Ler-0_sl-pool_FLT_Rep6_R2-FL-D3 | Atha_Ler-0_sl-pool_GC_Rep1_R1-GC-C2 | Atha_Ler-0_sl-pool_GC_Rep2_R1-GC-C5 | Atha_Ler-0_sl-pool_GC_Rep3_R1-GC-D3 | Atha_Ler-0_sl-pool_GC_Rep4_R2-GC-C2 | Atha_Ler-0_sl-pool_GC_Rep5_R2-GC-C5 | Atha_Ler-0_sl-pool_GC_Rep6_R2-GC-D3 | Atha_Ws-2_sl-pool_FLT_Rep1_R1-FL-C1 | Atha_Ws-2_sl-pool_FLT_Rep2_R1-FL-C4 | Atha_Ws-2_sl-pool_FLT_Rep3_R1-FL-D1 | Atha_Ws-2_sl-pool_FLT_Rep4_R1-FL-D4 | Atha_Ws-2_sl-pool_FLT_Rep5_R2-FL-C1 | Atha_Ws-2_sl-pool_FLT_Rep6_R2-FL-C4 | Atha_Ws-2_sl-pool_FLT_Rep7_R2-FL-D1 | Atha_Ws-2_sl-pool_FLT_Rep8_R2-FL-D4 | Atha_Ws-2_sl-pool_GC_Rep1_R1-GC-C1 | Atha_Ws-2_sl-pool_GC_Rep2_R1-GC-C4 | Atha_Ws-2_sl-pool_GC_Rep3_R1-GC-D1 | Atha_Ws-2_sl-pool_GC_Rep4_R1-GC-D4 | Atha_Ws-2_sl-pool_GC_Rep5_R2-GC-C1 | Atha_Ws-2_sl-pool_GC_Rep6_R2-GC-C4 | Atha_Ws-2_sl-pool_GC_Rep7_R2-GC-D1 | Atha_Ws-2_sl-pool_GC_Rep8_R2-GC-D4 |
| GLDS | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 | 37 |
| Accession | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 | GLDS-37 |
| Hardware | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC |
| Tissue | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling | Etiolated seedling |
| Age | 8 days | 8 days | 8 days | 8 days | 8 days | 8 days | 8 days | 8 days | 8 days | 8 days | 8 days | 8 days | 8 days | 8 days | 8 days | 8 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days |
| Organism | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana |
| Ecotype | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Cvi-0 | Cvi-0 | Cvi-0 | Cvi-0 | Cvi-0 | Cvi-0 | Cvi-0 | Cvi-0 | Cvi-0 | Cvi-0 | Cvi-0 | Cvi-0 | Ler-0 | Ler-0 | Ler-0 | Ler-0 | Ler-0 | Ler-0 | Ler-0 | Ler-0 | Ler-0 | Ler-0 | Ler-0 | Ler-0 | WS-2 | WS-2 | WS-2 | WS-2 | WS-2 | WS-2 | WS-2 | WS-2 | WS-2 | WS-2 | WS-2 | WS-2 | WS-2 | WS-2 | WS-2 | WS-2 |
| Genotype | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT |
| Variety | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Cvi-0 WT | Cvi-0 WT | Cvi-0 WT | Cvi-0 WT | Cvi-0 WT | Cvi-0 WT | Cvi-0 WT | Cvi-0 WT | Cvi-0 WT | Cvi-0 WT | Cvi-0 WT | Cvi-0 WT | Ler-0 WT | Ler-0 WT | Ler-0 WT | Ler-0 WT | Ler-0 WT | Ler-0 WT | Ler-0 WT | Ler-0 WT | Ler-0 WT | Ler-0 WT | Ler-0 WT | Ler-0 WT | WS-2 WT | WS-2 WT | WS-2 WT | WS-2 WT | WS-2 WT | WS-2 WT | WS-2 WT | WS-2 WT | WS-2 WT | WS-2 WT | WS-2 WT | WS-2 WT | WS-2 WT | WS-2 WT | WS-2 WT | WS-2 WT |
| Radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth |
| Gravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial |
| Developmental | 8 days old Seedlings | 8 days old Seedlings | 8 days old Seedlings | 8 days old Seedlings | 8 days old Seedlings | 8 days old Seedlings | 8 days old Seedlings | 8 days old Seedlings | 8 days old Seedlings | 8 days old Seedlings | 8 days old Seedlings | 8 days old Seedlings | 8 days old Seedlings | 8 days old Seedlings | 8 days old Seedlings | 8 days old Seedlings | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture |
| Time.series.or.Concentration.gradient | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point |
| Light | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark |
| Assay..RNAseq. | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling |
| Temperature | Ambient shuttle | Ambient shuttle | Ambient shuttle | Ambient shuttle | Ambient shuttle | Ambient shuttle | Ambient shuttle | Ambient shuttle | Ambient shuttle | Ambient shuttle | Ambient shuttle | Ambient shuttle | Ambient shuttle | Ambient shuttle | Ambient shuttle | Ambient shuttle | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS |
| Treatment.type | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight | Space flight |
| Treatment.intensity | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x |
| Treament.timing | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | Full Flight | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x |
| Preservation.Method. | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later | RNA later |
readData.out_ <- readData(inFile = inputFile_,
input_missingValue = "geneMedian",
input_dataFileFormat = 1,
input_minCounts = 0.5,
input_NminSamples = 1,
input_countsLogStart = 4,
input_CountsTransform = 1)
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
kable(head(readData.out_$data)) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Col0_FLT_Rep1 | Col0_FLT_Rep2 | Col0_FLT_Rep3 | Col0_FLT_Rep4 | Col0_FLT_Rep5 | Col0_FLT_Rep6 | Col0_FLT_Rep7 | Col0_FLT_Rep8 | Col0_GC_Rep1 | Col0_GC_Rep2 | Col0_GC_Rep3 | Col0_GC_Rep4 | Col0_GC_Rep5 | Col0_GC_Rep6 | Col0_GC_Rep7 | Col0_GC_Rep8 | Cvi0_FLT_Rep1 | Cvi0_FLT_Rep2 | Cvi0_FLT_Rep3 | Cvi0_FLT_Rep4 | Cvi0_FLT_Rep5 | Cvi0_FLT_Rep6 | Cvi0_GC_Rep1 | Cvi0_GC_Rep2 | Cvi0_GC_Rep3 | Cvi0_GC_Rep4 | Cvi0_GC_Rep5 | Cvi0_GC_Rep6 | Ler0_FLT_Rep1 | Ler0_FLT_Rep2 | Ler0_FLT_Rep3 | Ler0_FLT_Rep4 | Ler0_FLT_Rep5 | Ler0_FLT_Rep6 | Ler0_GC_Rep1 | Ler0_GC_Rep2 | Ler0_GC_Rep3 | Ler0_GC_Rep4 | Ler0_GC_Rep5 | Ler0_GC_Rep6 | Ws2_FLT_Rep1 | Ws2_FLT_Rep2 | Ws2_FLT_Rep3 | Ws2_FLT_Rep4 | Ws2_FLT_Rep5 | Ws2_FLT_Rep6 | Ws2_FLT_Rep7 | Ws2_FLT_Rep8 | Ws2_GC_Rep1 | Ws2_GC_Rep2 | Ws2_GC_Rep3 | Ws2_GC_Rep4 | Ws2_GC_Rep5 | Ws2_GC_Rep6 | Ws2_GC_Rep7 | Ws2_GC_Rep8 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AT2G41310 | 17.02215 | 17.01796 | 17.11739 | 16.48040 | 17.91059 | 17.26694 | 19.69218 | 19.20036 | 17.56076 | 17.72695 | 17.07714 | 18.37849 | 19.95574 | 19.38143 | 18.83440 | 19.12361 | 17.73175 | 17.44373 | 17.19451 | 19.12470 | 19.43718 | 19.58092 | 17.26674 | 17.25639 | 17.64822 | 19.63789 | 18.11876 | 20.17007 | 17.05219 | 17.65309 | 16.62785 | 20.37468 | 19.15037 | 18.03638 | 17.07419 | 18.36805 | 16.57124 | 19.70836 | 19.61946 | 20.43292 | 17.96129 | 19.66522 | 18.71354 | 19.00023 | 20.36251 | 21.61254 | 21.09230 | 17.93576 | 18.56609 | 18.10778 | 18.84677 | 18.34000 | 20.34275 | 21.00643 | 18.05459 | 18.47575 |
| ATCG00020 | 16.62437 | 17.17358 | 19.19315 | 17.01808 | 17.00478 | 18.04289 | 18.66012 | 18.34950 | 17.07015 | 18.45830 | 17.53183 | 17.54468 | 17.76015 | 18.99225 | 17.71870 | 18.08240 | 20.40519 | 21.10712 | 20.35049 | 20.54416 | 21.03219 | 20.10172 | 18.97992 | 17.80879 | 18.09481 | 19.14103 | 18.69038 | 18.63707 | 19.47328 | 18.93102 | 20.12098 | 19.48839 | 19.30928 | 19.93716 | 18.69104 | 17.14268 | 18.52542 | 19.24912 | 17.33441 | 18.56512 | 19.42824 | 18.91051 | 18.40429 | 18.42327 | 19.77389 | 18.61999 | 18.81388 | 18.75576 | 18.83601 | 16.97496 | 16.25988 | 18.60692 | 19.16008 | 17.90282 | 16.11539 | 17.61189 |
| ATCG00490 | 16.26692 | 16.68090 | 18.18697 | 17.15535 | 16.37882 | 17.29286 | 16.89119 | 17.94238 | 16.17645 | 17.48932 | 17.34631 | 17.44971 | 17.57745 | 17.66614 | 17.15341 | 17.90495 | 19.79300 | 20.45193 | 19.76313 | 19.77044 | 19.97784 | 19.27952 | 18.62011 | 17.41364 | 17.78610 | 18.62409 | 18.23437 | 18.10593 | 19.34302 | 19.30219 | 20.03934 | 19.20705 | 19.52952 | 19.63502 | 18.59304 | 17.10605 | 18.02425 | 19.69438 | 17.36523 | 18.63291 | 19.28034 | 18.37439 | 18.11924 | 18.45781 | 19.29917 | 18.23765 | 18.44432 | 18.19475 | 18.72367 | 16.56960 | 15.78959 | 17.92674 | 18.84758 | 17.51991 | 16.18827 | 17.89627 |
| ATCG00530 | 13.76395 | 14.01048 | 14.89293 | 15.23116 | 14.04617 | 13.85371 | 15.29187 | 12.60741 | 15.35153 | 13.92719 | 13.69533 | 15.25500 | 16.41620 | 13.83225 | 13.61397 | 16.25125 | 14.23795 | 14.81088 | 14.57108 | 15.10085 | 14.76077 | 13.92069 | 13.94028 | 13.73958 | 13.90483 | 13.68475 | 13.84517 | 13.85507 | 14.20240 | 14.24318 | 13.94380 | 15.47188 | 14.89890 | 14.54820 | 14.21203 | 15.39214 | 15.26377 | 14.20046 | 13.78132 | 16.23040 | 16.26147 | 17.29414 | 16.61576 | 16.69669 | 17.20347 | 19.35494 | 18.03140 | 19.94179 | 14.88256 | 16.18132 | 16.92704 | 15.15767 | 15.39250 | 15.08054 | 15.84510 | 14.21931 |
| ATCG00740 | 14.73210 | 14.94219 | 14.96504 | 16.04677 | 14.54676 | 14.17489 | 15.18165 | 13.11581 | 15.64299 | 13.96589 | 13.91204 | 15.53611 | 17.04230 | 13.79330 | 14.20803 | 16.25587 | 15.05854 | 14.88059 | 14.75081 | 14.86381 | 14.25578 | 13.70575 | 13.68758 | 13.93228 | 14.10991 | 13.79351 | 13.79747 | 14.30696 | 14.88986 | 15.22620 | 14.16816 | 15.53573 | 14.88442 | 14.69631 | 15.62069 | 16.03472 | 16.02725 | 14.50385 | 14.76714 | 16.70364 | 16.58074 | 18.22371 | 17.31301 | 17.44096 | 17.00944 | 18.87289 | 17.40664 | 19.25440 | 15.67269 | 16.61073 | 17.15054 | 15.90107 | 15.14634 | 15.87748 | 15.79090 | 15.44628 |
| ATCG00650 | 13.96212 | 13.97166 | 14.27354 | 14.49403 | 14.36924 | 13.62418 | 14.69982 | 12.46797 | 14.94863 | 12.81904 | 12.93448 | 14.71847 | 15.66234 | 12.58834 | 13.64333 | 15.52227 | 14.05529 | 13.79046 | 14.04351 | 13.94000 | 13.25782 | 12.82255 | 12.96584 | 13.25719 | 12.99900 | 13.09773 | 12.91592 | 13.77193 | 14.15583 | 14.28258 | 13.15073 | 14.61523 | 14.36325 | 13.67559 | 14.85099 | 15.08337 | 15.65327 | 13.61956 | 14.15712 | 16.15821 | 16.43927 | 17.93933 | 17.06040 | 16.89910 | 16.60920 | 18.72510 | 17.42999 | 18.99587 | 15.30840 | 16.78608 | 16.80205 | 15.32805 | 14.87381 | 15.78032 | 15.27499 | 13.68067 |
readSampleInfo.out_ <- readSampleInfo(inFile = sampleInfoFile_,
readData.out = readData.out_)
kable(readSampleInfo.out_) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Gravity | Variety | |
|---|---|---|
| Col0_FLT_Rep1 | FLT | Col0 |
| Col0_FLT_Rep2 | FLT | Col0 |
| Col0_FLT_Rep3 | FLT | Col0 |
| Col0_FLT_Rep4 | FLT | Col0 |
| Col0_FLT_Rep5 | FLT | Col0 |
| Col0_FLT_Rep6 | FLT | Col0 |
| Col0_FLT_Rep7 | FLT | Col0 |
| Col0_FLT_Rep8 | FLT | Col0 |
| Col0_GC_Rep1 | GC | Col0 |
| Col0_GC_Rep2 | GC | Col0 |
| Col0_GC_Rep3 | GC | Col0 |
| Col0_GC_Rep4 | GC | Col0 |
| Col0_GC_Rep5 | GC | Col0 |
| Col0_GC_Rep6 | GC | Col0 |
| Col0_GC_Rep7 | GC | Col0 |
| Col0_GC_Rep8 | GC | Col0 |
| Cvi0_FLT_Rep1 | FLT | Cvi0 |
| Cvi0_FLT_Rep2 | FLT | Cvi0 |
| Cvi0_FLT_Rep3 | FLT | Cvi0 |
| Cvi0_FLT_Rep4 | FLT | Cvi0 |
| Cvi0_FLT_Rep5 | FLT | Cvi0 |
| Cvi0_FLT_Rep6 | FLT | Cvi0 |
| Cvi0_GC_Rep1 | GC | Cvi0 |
| Cvi0_GC_Rep2 | GC | Cvi0 |
| Cvi0_GC_Rep3 | GC | Cvi0 |
| Cvi0_GC_Rep4 | GC | Cvi0 |
| Cvi0_GC_Rep5 | GC | Cvi0 |
| Cvi0_GC_Rep6 | GC | Cvi0 |
| Ler0_FLT_Rep1 | FLT | Ler0 |
| Ler0_FLT_Rep2 | FLT | Ler0 |
| Ler0_FLT_Rep3 | FLT | Ler0 |
| Ler0_FLT_Rep4 | FLT | Ler0 |
| Ler0_FLT_Rep5 | FLT | Ler0 |
| Ler0_FLT_Rep6 | FLT | Ler0 |
| Ler0_GC_Rep1 | GC | Ler0 |
| Ler0_GC_Rep2 | GC | Ler0 |
| Ler0_GC_Rep3 | GC | Ler0 |
| Ler0_GC_Rep4 | GC | Ler0 |
| Ler0_GC_Rep5 | GC | Ler0 |
| Ler0_GC_Rep6 | GC | Ler0 |
| Ws2_FLT_Rep1 | FLT | WS2 |
| Ws2_FLT_Rep2 | FLT | WS2 |
| Ws2_FLT_Rep3 | FLT | WS2 |
| Ws2_FLT_Rep4 | FLT | WS2 |
| Ws2_FLT_Rep5 | FLT | WS2 |
| Ws2_FLT_Rep6 | FLT | WS2 |
| Ws2_FLT_Rep7 | FLT | WS2 |
| Ws2_FLT_Rep8 | FLT | WS2 |
| Ws2_GC_Rep1 | GC | WS2 |
| Ws2_GC_Rep2 | GC | WS2 |
| Ws2_GC_Rep3 | GC | WS2 |
| Ws2_GC_Rep4 | GC | WS2 |
| Ws2_GC_Rep5 | GC | WS2 |
| Ws2_GC_Rep6 | GC | WS2 |
| Ws2_GC_Rep7 | GC | WS2 |
| Ws2_GC_Rep8 | GC | WS2 |
input_noIDConversion_ <- TRUE
allGeneInfo.out_ <- geneInfo(fileName = geneInfoFile_)
converted.out_ <- NULL
convertedData.out_ <- convertedData(converted.out = NULL,
readData.out = readData.out_,
input_noIDConversion = TRUE)
nGenesFilter(readData.out = readData.out_,
converted.out = NULL,
convertedData.out = convertedData.out_,
input_noIDConversion = TRUE)
## [1] "16156 genes in 56 samples. 16121 genes passed filter.\n Original gene IDs used."
convertedCounts.out_ <- convertedCounts(readData.out = readData.out_, converted.out = NULL) # converted counts, just for compatibility
# Read counts per library
parDefault_ <- par()
par(mar = c(12, 4, 2, 2))
# barplot of total read counts
rawCounts <- readData.out_$rawCounts
groups_ <- as.factor(detectGroups(colnames(rawCounts)))
if (nlevels(groups_) <= 1 | nlevels(groups_) > 20) {
col1_ <- "green"
} else {
col1_ <- rainbow(nlevels(groups_))[groups_]
}
barplot(colSums(readData.out_$rawCounts) / 1e6,
col = col1_, las = 3, main = "Total read counts (millions)"
)
readCountsBias(readData.out = readData.out_, readSampleInfo.out = readSampleInfo.out_) # detecting bias in sequencing depth
## [1] 5.946657e-07
## [1] 0.0008267088
## [1] 0.003814545
## [1] "Warning! Sequencing depth bias detected. Total read counts are significantly different among sample groups (p= 5.95e-07 ) based on ANOVA. Total read counts seem to be correlated with factor Gravity (p= 8.27e-04 ). Total read counts seem to be correlated with factor Variety (p= 3.81e-03 ). "
# Box plot
boxplot(
x = readData.out_$data,
las = 2, col = col1_,
ylab = "Transformed expression levels",
main = "Distribution of transformed data"
)
# Density plot
par(parDefault_)
## Warning in par(parDefault_): graphical parameter "cin" cannot be set
## Warning in par(parDefault_): graphical parameter "cra" cannot be set
## Warning in par(parDefault_): graphical parameter "csi" cannot be set
## Warning in par(parDefault_): graphical parameter "cxy" cannot be set
## Warning in par(parDefault_): graphical parameter "din" cannot be set
## Warning in par(parDefault_): graphical parameter "page" cannot be set
densityPlot(readData.out = readData.out_,
mycolors = mycolors_)
# Scatter plot of the first two samples
plot(
x = readData.out_$data[, 1:2],
xlab = colnames(readData.out_$data)[1],
ylab = colnames(readData.out_$data)[2],
main = "Scatter plot of first two samples"
)
#### plot gene or gene family
genePlot(allGeneInfo.out = allGeneInfo.out_,
convertedData.out = convertedData.out_,
input_selectOrg = "BestMatch",
input_geneSearch = "HOXA")
## NULL
geneBarPlotError(convertedData.out = convertedData.out_,
allGeneInfo.out = allGeneInfo.out_,
input_selectOrg = 'BestMatch',
input_geneSearch = "HOXA",
input_useSD = "FALSE") # Use standard deviation instead of standard error in error bar?
## NULL
# hierarchical clustering tree
x <- readData.out_$data
maxGene <- apply(x, 1, max)
# remove bottom 25% lowly expressed genes, which inflate the PPC
x <- x[which(maxGene > quantile(maxGene)[1]), ]
plot(as.dendrogram(hclust2(dist2(t(x)))), ylab = "1 - Pearson C.C.", type = "rectangle")
# Correlation matrix
#input_labelPCC_ <- TRUE # Show correlation coefficient?
correlationMatrix(readData.out = readData.out_, input_labelPCC = TRUE)
png(paste(params$input_folder, "heatmap.png", sep = "/"), width = 10, height = 15, units = "in", res = 300)
staticHeatmap(readData.out = readData.out_,
readSampleInfo.out = readSampleInfo.out_,
heatColors = heatColors_,
input_nGenes = 1000,
input_geneCentering = TRUE,
input_sampleCentering = FALSE,
input_geneNormalize = FALSE,
input_sampleNormalize = FALSE,
input_noSampleClustering = FALSE,
input_heatmapCutoff = 4,
input_distFunctions = 1,
input_hclustFunctions = 1,
input_heatColors1 = 1,
input_selectFactorsHeatmap = 'Gravity')
dev.off()
## quartz_off_screen
## 2
[heatmap] (GLDS37/heatmap.png)
heatmapPlotly(convertedData.out = convertedData.out_,
heatColors = heatColors_,
allGeneInfo.out = allGeneInfo.out_,
input_geneCentering = TRUE,
input_sampleCentering = FALSE,
input_geneNormalize = FALSE,
input_sampleNormalize = FALSE,
input_heatColors1 = 1)# interactive heatmap using Plotly
distributionSD(convertedData.out = convertedData.out_,
input_nGenesKNN = 2000) # Distribution of standard deviations
KmeansNclusters(convertedData.out = convertedData.out_,
input_nGenesKNN = 2000) # Number of clusters
Kmeans.out_ <- Kmeans(convertedData.out = convertedData.out_,
maxGeneClustering = 12000,
input_nGenesKNN = 2000,
input_nClusters = 4,
input_kmeansNormalization = "geneMean",
input_KmeansReRun = 0) # Running K-means
KmeansHeatmap(Kmeans.out = Kmeans.out_,
.mycolors = mycolors_,
.heatColors = heatColors_,
.input_heatColors1 = 1) # Heatmap for k-Means
# Read gene sets for enrichment analysis
GeneSets.out_ <- readGeneSets(
fileName = geneSetFile_,
convertedData = convertedData.out_,
GO = "GOBP",
selectOrg = "NEW",
myrange = c(15, 2000)
)
# Alternatively, users can use their own GMT files by
# GeneSets.out_ <- readGMTRobust('somefile.GMT')
results <- KmeansGO(Kmeans.out = Kmeans.out_,
input_nClusters = 4,
GeneSets.out = GeneSets.out_) # Enrichment analysis for k-Means clusters
results$adj.Pval <- format(results$adj.Pval, digits = 3)
kable(results, row.names = FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Cluster | adj.Pval | Genes | Pathways |
|---|---|---|---|
| A | 4.45e-62 | 193 | Response to abiotic stimulus |
| 1.19e-52 | 173 | Response to organic substance | |
| 1.08e-48 | 115 | Response to inorganic substance | |
| 2.10e-45 | 147 | Response to hormone | |
| 2.75e-45 | 148 | Response to endogenous stimulus | |
| 9.13e-45 | 81 | Response to metal ion | |
| 4.28e-42 | 69 | Response to cadmium ion | |
| 2.75e-40 | 143 | Organonitrogen compound biosynthetic process | |
| 7.20e-40 | 97 | Amide biosynthetic process | |
| 6.27e-38 | 102 | Cellular amide metabolic process | |
| B | 2.02e-108 | 78 | Photosynthesis |
| 2.29e-58 | 43 | Photosynthesis, light reaction | |
| 5.69e-48 | 53 | Generation of precursor metabolites and energy | |
| 2.33e-32 | 22 | Photosynthetic electron transport chain | |
| 2.36e-31 | 73 | Response to abiotic stimulus | |
| 4.18e-29 | 60 | Oxidation-reduction process | |
| 7.24e-28 | 29 | Electron transport chain | |
| 3.95e-27 | 62 | Organonitrogen compound biosynthetic process | |
| 3.70e-26 | 18 | Protein-chromophore linkage | |
| 4.39e-24 | 40 | Response to light stimulus | |
| C | 4.50e-35 | 91 | Response to oxygen-containing compound |
| 2.70e-31 | 99 | Response to abiotic stimulus | |
| 1.74e-28 | 80 | Cellular response to chemical stimulus | |
| 1.64e-27 | 70 | Response to acid chemical | |
| 3.24e-26 | 88 | Response to organic substance | |
| 1.33e-23 | 58 | Response to inorganic substance | |
| 5.08e-22 | 37 | Response to water | |
| 5.08e-22 | 30 | Cellular response to decreased oxygen levels | |
| 5.08e-22 | 30 | Cellular response to oxygen levels | |
| 5.08e-22 | 30 | Cellular response to hypoxia | |
| D | 5.13e-27 | 114 | Response to abiotic stimulus |
| 4.74e-19 | 86 | Response to hormone | |
| 4.90e-19 | 63 | Response to inorganic substance | |
| 5.55e-19 | 95 | Response to organic substance | |
| 5.77e-19 | 86 | Response to endogenous stimulus | |
| 7.87e-18 | 79 | Cellular response to chemical stimulus | |
| 1.20e-17 | 81 | Response to oxygen-containing compound | |
| 6.77e-15 | 64 | Response to acid chemical | |
| 8.33e-15 | 46 | Response to osmotic stress | |
| 1.30e-14 | 56 | Cellular amide metabolic process |
tSNEgenePlot(Kmeans.out_,
input_seedTSNE = 0,
input_colorGenes = TRUE, # Color genes in t-SNE plot?
mycolors = mycolors_) # Plot genes using t-SNE
PCAplot(convertedData.out = convertedData.out_,
readSampleInfo.out = readSampleInfo.out_,
input_selectFactors = colnames(readSampleInfo.out_)[1],
input_selectFactors2 = colnames(readSampleInfo.out_)[2])
MDSplot(convertedData.out = convertedData.out_,
readSampleInfo.out = readSampleInfo.out_,
input_selectFactors = colnames(readSampleInfo.out_)[1],
input_selectFactors2 = colnames(readSampleInfo.out_)[2])
tSNEplot(convertedData.out = convertedData.out_,
readSampleInfo.out = readSampleInfo.out_,
input_selectFactors = colnames(readSampleInfo.out_)[1],
input_selectFactors2 = colnames(readSampleInfo.out_)[2],
input_tsneSeed2 = 0)
# Read gene sets for pathway analysis using PGSEA on principal components
GeneSets.out_ <- readGeneSets(
fileName = geneSetFile_,
convertedData = convertedData.out_,
GO = "GOBP",
selectOrg = "NEW",
myrange = c(15, 2000)
)
PCApathway(convertedData.out = convertedData.out_,
GeneSets.out = GeneSets.out_) # Run PGSEA analysis
cat(
PCA2factor(readData.out = readData.out_,
readSampleInfo.out = readSampleInfo.out_)
) # The correlation between PCs with factors
##
## Correlation between Principal Components (PCs) with factors
## PC1 is correlated with Variety (p=7.62e-14).
## PC2 is correlated with Variety (p=9.29e-14).
## PC3 is correlated with Gravity (p=2.56e-05).
# List to hold limma outputs for all variables
limma_outputs <- setNames(as.list(rep(NA, ncol(readSampleInfo.out_))),
nm = colnames(readSampleInfo.out_))
# List to hold all comparisons for each variable
comps <- limma_outputs
# List to hold all DEG.out for each variable
DEG_output <- limma_outputs
for (variable in colnames(readSampleInfo.out_)){
values <- unique(readSampleInfo.out_[, variable])
# Create all combinations for this variable
all_comps <- outer(values, values, function(x,y) paste0(variable, ": ", x, " vs. ", y))
comps[[variable]] <- all_comps[upper.tri(all_comps)]
}
# Run limma for all variables. lapply runs the function for each element of the list that you provide.
limma_outputs <- lapply(comps,
function(x)
limma(convertedData.out = convertedData.out_,
readSampleInfo.out = readSampleInfo.out_,
input_dataFileFormat = 1,
input_countsLogStart = 4,
convertedCounts.out = convertedCounts.out_,
input_CountsDEGMethod = 3, # 3
input_limmaPval = 0.1,
input_limmaFC = 3,
input_selectModelComprions = x,
input_selectFactorsModel = unlist(strsplit(x[1], split = ":"))[1],
input_selectInteractions = NULL,
input_selectBlockFactorsModel = NULL,
factorReferenceLevels.out = NULL)
)
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
DEG.data_out <- lapply(limma_outputs,
function(x)
DEG.data(limma.out = x,
convertedData.out = convertedData.out_,
allGeneInfo.out = allGeneInfo.out_))
for (var in names(limma_outputs)){
# Print header for subsection
cat("### Limma for", var)
cat("\n\n")
# Print all comparisons done.
cat("Comparisons done for this variable:\n")
cat(limma_outputs[[var]]$comparisons, sep = ", ")
cat("\n")
## Venn diagram.
nVennGroups <- min(params$nVennGroupsMax, length(limma_outputs[[var]]$comparisons)) # if less than three comparisons, include all comparisons.
vennPlot(limma.out = limma_outputs[[var]],
input_selectComparisonsVenn = limma_outputs[[var]]$comparisons[1:nVennGroups],
input_UpDownRegulated = FALSE) # Split up and down regulated genes
print(sigGeneStats(limma_outputs[[var]])) # number of DEGs as figure
kable(sigGeneStatsTable(limma_outputs[[var]]),
row.names = FALSE) %>% # number of DEGs as table
kable_styling(bootstrap_options = c("striped", "hover"), position = "center") %>%
print()
}
| Comparisons | Up | Down |
|---|---|---|
| FLT-GC | 46 | 38 |
| Comparisons | Up | Down |
|---|---|---|
| Col0-Cvi0 | 315 | 353 |
| Col0-Ler0 | 287 | 292 |
| Cvi0-Ler0 | 72 | 94 |
| Col0-WS2 | 290 | 296 |
| Cvi0-WS2 | 289 | 287 |
| Ler0-WS2 | 267 | 264 |